Postgraduate Institute of Medicine University of Colombo

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Identifying and Visualizing Gene-Gene Interactions Associated With Myocardial Infarction Myocardial infarction is an important clinical problem because of its large contribution to mortality. Recent studies have shown the importance of genetic factors and the interaction between multiple genes and environmental factors in the susceptibility to the disease. Disease prevention is an important strategy for reducing the overall burden of myocardial infarction, with the identification of markers for disease risk being key for both risk prediction and for potential intervention to lower the chance of future events. The increasing availability and diversity of omics (genomics and proteomics) data in the postgenomic era from various interaction studies offers new perspectives in most areas of biomedical research. This study, was aimed at identifying the interactions between the various genes associated with myocardial infarction, using data retrieved from high-throughput experiments and literature available from databases such as HuGE Navigator, Online Mendelian Inheritance in Man Morbid Map, PubMed and GeneCards. Graph-based biological networks models capture the topology of the functional relationships between molecular entities such as gene, protein and small compounds and provide a suitable framework for integrating and analyzing these data. The genes associated with myocardial infarction were identified from the biological data repositories and their interaction was visualized using, BisoGenet, one of the plugins of Cytoscape, an open-source software. The interaction network of the six most important genes (nodes with more connections) that were found to interact and affect the susceptibility to myocardial infarction was visualized using Cytoscape. These interactions could provide new insights into the multigenic architecture of myocardial infarction. Furthermore, it may be possible to prevent an individual from having myocardial infarction by medical intervention based on his or her genotype for specific polymorphisms. BisoGenet, the tool that was selected was use to build and visualize biological networks in a fast and user-friendly manner. Dr. K Thavayogarajah thavasabapathy@hotmail.com Supervisors: Prof. Vajira H. W. Dissanayake MBBS, PhD (Nottingham) Chairperson, Specialty Board in Biomedical Informatics Mr. Pubudu Samarakoon BSc, MSc (Bioinformatics) Member, Specialty Board in Biomedical Informatics 49 | P a g e
Identification of Gene Interactions Associated with Breast Cancer A complex disease such as breast cancer has a multigenic aetiology. Discovering breast cancer specific genes require laborious and time consuming laboratory experiments. Therefore, it is a good idea to identify an appropriate initial set of genes that can be useful to perform appropriate laboratory experiments. This work was conducted identify such genes by studying gene-gene interactions. Literature based gene-gene interaction identification was carried out manually to extract gene-gene interactions from PubMed abstracts. Then, a gene-gene interaction network was visualized using the Cytoscape visualization tool. Gene ranking was done using network analysis. The gene-gene interaction network was used as a base for the development of an interactive web based module to access gene information. Dr. W.F.D. Ananda fdananda1234@gmail.com Supervisors: Prof. Vajira H. W. Dissanayake MBBS, PhD (Nottingham) Chairperson, Specialty Board in Biomedical Informatics Mr. Pubudu Samarakoon BSc, MSc (Bioinformatics) Member, Specialty Board in Biomedical Informatics 50 | P a g e
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